Computing devices continue to become more ubiquitous to daily life. They take the form of computer desktops, laptop computers, tablet computers, e-book readers, mobile phones, smartphones, wearable computers, global positioning system (GPS) units, enterprise digital assistants (EDAs), personal digital assistants (PDAs), game consoles, and the like. Further, computing devices are being incorporated into vehicles and equipment, such as cars, trucks, farm equipment, manufacturing equipment, building environment control (e.g., lighting, HVAC), and home and commercial appliances.
Computing devices generally consist of at least one processing element, such as a central processing unit (CPU), some form of memory, and input and output devices. The variety of computing devices and their subsequent uses necessitate a variety of interfaces and input devices. One such input device is a touch sensitive surface such as a touch screen or touch pad wherein user input is received through contact between the user's finger or an instrument such as a pen or stylus and the touch sensitive surface. Another input device is an input surface that senses gestures made by a user above the input surface. Either of these methods of input can be used generally for drawing or inputting text. When user input is text the user's handwriting is interpreted using a handwriting recognition system or method.
One application of handwriting recognition in portable computing devices, such as smartphones, phablets and tablets, is in note taking. This particularly occurs in education and business settings where the user of the computing device captures notes, for example, during a lecture or meeting. This is usually done by the user launching a handwritten note taking application on the computing device which accepts and interprets, either locally in the device or remotely via a communications link of the device, handwritten notes input on the touch sensitive surface. Conventionally such handwritten note taking applications provide recognition of handwriting and conversion of the recognized handwriting, so-called digital ink, into typeset text. The Applicant has found that when using handwriting note taking applications users typically desire real-time feedback of the recognition, that is during writing. Some conventional applications provide such feedback mechanisms to users but these are generally limited in their effectiveness or are cumbersome in design.
For example, available handwritten note taking applications provide feedback by typesetting the handwritten input automatically (e.g., on-the-fly). This is typically done by displaying the typeset text in a window or dedicated portion of the device display which is separate from the interface for handwriting input. However, such systems generally distract users from the flow of input since they must look at different parts of the screen. Other available handwritten note taking applications provide feedback by listing recognition candidates and the like during input. This is also very distracting for users.
It is possible to convert the digital ink into typeset text in situ, e.g., behind the handwriting flow. However, typeset text is generally smaller and more uniform than handwritten text. Accordingly, the relative difference in character, word and line spacing quickly becomes an issue, which makes the recognized text difficult to read and therefore distracting. It is also possible to enable users to manually typeset when desired through menu selections and the input of gestures. However, such manual methods do not provide real-time feedback and the Applicant has found that when using handwriting note taking applications users generally are unable or do not desire to learn specific input methods or gestures that are not natural or intuitive, or to make settings through menus and the like.
Further, the conventional applications do not to provide users with the ability to interact with the input or provide full document creation from the notes taken, since the focus of these applications has primarily been recognition accuracy rather than document creation. Further, the Applicant has found that when using handwriting note taking applications users experience other issues as well, such as, writing notes in multiple languages or input types (e.g., alphanumeric, symbols, equations) and forgetting to change languages or input types via menu selections when writing, and not understanding the constraints on writing dimensions e.g., height, length and amount, imposed by the applications due to display constraints and the like.